title: 2DPCA with L1-norm for simultaneously robust and sparse modelling.
abstract: Robust dimensionality reduction is an important issue in processing multivariate data. Two-dimensional principal component analysis based on L1-norm (2DPCA-L1) is a recently developed technique for robust dimensionality reduction in the image domain. The basis vectors of 2DPCA-L1, however, are still dense. It is beneficial to perform a sparse modelling for the image analysis. In this paper, we propose a new dimensionality reduction method, referred to as 2DPCA-L1 with sparsity (2DPCAL1-S), which effectively combines the robustness of 2DPCA-L1 and the sparsity-inducing lasso regularization. It is a sparse variant of 2DPCA-L1 for unsupervised learning. We elaborately design an iterative algorithm to compute the basis vectors of 2DPCAL1-S. The experiments on image data sets confirm the effectiveness of the proposed approach.
authors.affiliation: Key Laboratory of Child Development and Learning Science of Ministry of Education, Research Center for Learning Science, Southeast University, Nanjing, Jiangsu 210096, PR China. hxwang@seu.edu.cn
authors.forename: Haixian
authors.initials: H
authors.lastname: Wang
authors.forename: Jing
authors.initials: J
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D005145:Face / Q000502:physiology; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods*; D025341:Principal Component Analysis / Q000379:methods
keywords: 2DPCA-L1; Dimensionality reduction; Lasso regularization; Robust modelling